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Biology subjects

Bernard, T.

Publications and source records attributed to Bernard, T..

2 recordsLinked to original sources

β-lactam antibiotics trigger TLR2-dependent Jarisch-Herxheimer reaction in leptospirosis: bacteriostatic antibiotics offer a safer alternative

Leptospirosis is a neglected zoonotic disease causing ~1 million cases and 60,000 deaths annually. Antibiotic therapy can trigger a detrimental inflammatory Jarisch-Herxheimer reaction (JHR). We investigated antibiotic effects in leptospirosis, using a mouse model of severe disease and human whole blood from healthy donors infected ex vivo with bioluminescent Leptospira interrogans. We tested the effect of amoxicillin and ceftriaxone, two bactericidal beta-Lactams antibiotics and assessed bacterial survival, cytokines, pathophysiology and JHR mechanisms upon amoxicillin. Beta-lactams induced profound pro-inflammatory cytokine release. In mice, the inflammation was TLR2 and TLR4 dependent, accordingly to the TLR4 host-specificity of LPS recognition, and amoxicillin exacerbated disease severity within hours, notably worsening myocarditis. In human whole blood, this inflammation depended largely on TLR2 (lipoprotein receptor) and TLR5 (flagellin receptor). Interestingly, only stealthy virulent isolates of Leptospira interrogans, unlike some Leptospira species belonging to intermediate and non-virulent clades such as biflexa, induced in human whole blood a JHR-like effect, which was also observed with Borrelia burgdorferi. These findings demonstrate that host-specific TLR recognition of released spirochaetal components by antibiotic treatment drives JHR, evoking a cytokine storm and worsening outcomes.

immunology↗

Correlated Evolution of Metabolic Functions over the Tree of Life

We are interested in the structure and evolution of metabolism in order to better understand its complexity. We study metabolic functions in 1459 species within which several hundreds of thousands of families of homologous genes have been identified [17]. Given a protein sequence, PRIAM search [5] delivers probabilities of the presence of several thousand enzymes (ECs). This allows us to infer reaction sets and to construct a metabolic network for an organism, given its set of sequences.\n\nWe then propagate these ECs to the ancestral nodes of the species tree using maximimum likelihood methods. These evolutionary scenarios are systematically compared using pairwise mutual information. We identify co-evolving enzyme sets from the graph of these relationships using community detection algorithms [1,3]. This sheds light on the structure of the metabolic networks in terms of co-evolving metabolic modules. These modules are also interpreted from a functional perspective using stoichiometric models of metabolic networks.

evolutionary biology↗